CN115237147B - Vehicle longitudinal distance control method - Google Patents
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
Abstract
The invention discloses a vehicle longitudinal distance control method, which relates to the technical field of automatic driving based on vehicle-road cooperation, and comprises the following steps: performing multi-sensor information fusion on targets acquired by a road end sensor and a vehicle end sensor, and screening out a plurality of targets after information fusion; determining a front vehicle following target of the self vehicle from the screened targets after the information fusion; the method comprises the steps of (1) establishing a sliding mode surface by establishing a vehicle dynamics control equation based on an exponential approximation law vehicle sliding mode control distance algorithm, and calculating to obtain the ideal following acceleration of a vehicle based on the exponential approximation law; and controlling the self vehicle to run at the ideal following acceleration so as to control the longitudinal distance between the self vehicle and the front following target. The invention can more accurately, real-timely and stably complete the control of the longitudinal distance and improve the stability and accuracy of the control of the longitudinal distance.
Description
Technical Field
The invention relates to the technical field of automatic driving based on vehicle-road cooperation, in particular to a method for controlling the longitudinal distance of a vehicle.
Background
The intelligent automobile longitudinal control means that the adjustment of the automobile longitudinal speed is realized through a certain control method according to the information acquired by the vehicle-mounted sensing system, the automatic longitudinal acceleration and deceleration function of the intelligent automobile is realized, and the quality of the autonomous driving performance of the intelligent automobile is determined. Because pure delay, time lag and coupling characteristics exist in a power source system of the intelligent automobile, and the automobile longitudinal dynamic model also has parameter uncertainty and strong nonlinear dynamic characteristics and can be interfered by external environments such as air resistance, road gradient and the like, the design of a longitudinal control method becomes extremely difficult.
The sliding mode control technology is a common method for designing the intelligent automobile longitudinal controller at present, and has strong robustness to external interference and model nonlinearity. Patent CN201610527920.8 mentions a learning-based intelligent automobile longitudinal neural sliding mode control method, but the control output is accelerator opening or brake pressure, which is not in line with the architecture of acceleration as control output in the traditional automobile industry. Patent CN201510278571.6 mentions a method for unmanned vehicle side longitudinal coupling tracking control based on a fast sliding mode principle, and a desired throttle opening or braking torque is finally calculated by using a fast terminal sliding mode principle, but the final torque output in the traditional automobile industry generally needs to be arbitrated by an ESP and finally is decided to a power system or a braking system, so that the method is not very consistent with the development framework of an actual intelligent driving system. Meanwhile, in the prior art, the longitudinal distance of the vehicle is controlled by utilizing vehicle-end perception, and the control of the longitudinal distance cannot be accurately, timely and stably finished in the case of sudden change of a front-end vehicle, such as a blind area crossing into a lane and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a vehicle longitudinal distance control method.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a vehicle longitudinal spacing control method, comprising:
performing multi-sensor information fusion on targets acquired by a road end sensor and a vehicle end sensor, and screening out a plurality of targets subjected to information fusion;
determining a front vehicle following target of the self vehicle from the screened targets after the information fusion;
the method comprises the steps of (1) establishing a sliding mode surface by establishing a vehicle dynamics control equation based on an exponential approximation law vehicle sliding mode control distance algorithm, and calculating to obtain the ideal following acceleration of a vehicle based on the exponential approximation law;
and controlling the self-vehicle to run at the ideal following acceleration so as to control the longitudinal distance between the self-vehicle and the front following target.
As a preferable aspect of the vehicle longitudinal distance control method of the present invention, wherein: the multi-sensor information fusion of the road end sensor and the target collected by the vehicle end sensor comprises the following steps:
performing information inspection on targets acquired by the road end sensor and the vehicle end sensor, and filtering and removing the targets which do not meet the requirements;
sequencing according to the timestamps of the road end sensor and the vehicle end sensor, and circularly entering the acquired targets into a linear Kalman filter for target fusion.
As a preferable aspect of the vehicle longitudinal distance control method of the present invention, wherein: screening out a plurality of targets after information fusion comprises the following steps:
and (4) carrying out priority ordering on the targets subjected to information fusion according to the confidence, the Mahalanobis distance, the position standard deviation, the speed standard deviation, the acceleration standard deviation and the fusion state of linear Kalman filtering, and screening out a plurality of targets from high to low according to the priority.
As a preferable aspect of the vehicle longitudinal distance control method of the invention, wherein: the step of determining the front vehicle-following target of the self vehicle from the screened targets after information fusion comprises the following steps:
filtering and removing targets which do not meet the requirement of the following targets in front of the self vehicle;
predicting the track of the self-vehicle according to the self-vehicle kinematics module, and calculating the transverse position deviation of the screened target and the self-vehicle;
substituting the transverse position deviation into a lane contour model to determine a target which is closest to the own vehicle in a left lane, a target which is closest to the own vehicle in a right lane and a target which is closest to the own vehicle in a lane where the own vehicle is located;
predicting the time from lane change to lane of the vehicle based on the transverse position and the transverse speed of the target in the left lane closest to the vehicle and the target in the right lane closest to the vehicle;
and comparing the predicted time with a preset threshold, if the predicted time is greater than the threshold, taking a target closest to the vehicle in a lane where the vehicle is located as a front vehicle following target of the vehicle, and if the predicted time is less than the threshold, taking a corresponding target as the front vehicle following target of the vehicle.
As a preferable aspect of the vehicle longitudinal distance control method of the present invention, wherein: the vehicle sliding mode control vehicle distance algorithm based on the exponential approximation law establishes a vehicle dynamics control equation and a sliding mode surface, and calculates and obtains the ideal following acceleration of the self vehicle based on the exponential approximation law, and comprises the following steps:
establishing a dynamic model for describing the longitudinal motion characteristics of the self-vehicle;
establishing a slip form surface equation, so that points outside the slip form surface can move to the slip form surface under a certain condition;
setting an index approach law to enable any initial point to approach to the slip form surface;
preliminarily solving a control law equation according to the dynamic model, the sliding mode surface equation and the exponential approximation law;
smoothing the control law equation, and adding a feedforward term;
and calculating to obtain the ideal following acceleration of the self vehicle according to the control law equation.
As a preferable aspect of the vehicle longitudinal distance control method of the present invention, wherein: the establishing of the dynamic model describing the longitudinal motion characteristics of the self-vehicle comprises the following steps:
the following kinetic model was established:
wherein, in the step (A),
representing the actual distance between the vehicle and the front following target,
represents the ideal distance between the self vehicle and the front following vehicle target,
the safe time interval between the self vehicle and the front following target is shown,
indicates the vehicle speed of the preceding following vehicle target,
representing the difference between the actual spacing and the ideal spacing,
the relative speed of the vehicle and the front following target is shown, and u represents the ideal following acceleration of the vehicle.
As a preferable aspect of the vehicle longitudinal distance control method of the invention, wherein: the establishing of the sliding mode surface equation to enable the point outside the sliding mode surface to move to the sliding mode surface through a certain condition comprises the following steps:
the following sliding mode surface equation is established:
wherein, in the process,
、
、
is a set parameter.
As a preferable aspect of the vehicle longitudinal distance control method of the invention, wherein: the setting of the index approach law enables any initial point to approach the slip form surface, and comprises the following steps:
the exponential approximation law is set as follows:
wherein k is an exponential approximation term coefficient.
As a preferable aspect of the vehicle longitudinal distance control method of the invention, wherein: according to the dynamic model, the sliding mode surface equation and the exponential approximation law, the preliminary solution of the control law equation comprises the following steps:
the control law equation is:
。
as a preferable aspect of the vehicle longitudinal distance control method of the present invention, wherein: smoothing the control law equation, and adding a feed-forward term comprises:
and smoothing the control law equation to obtain:
；
adding a feed forward term to yield:
wherein, in the process,
the acceleration of the front following target.
The beneficial effects of the invention are:
the invention can acquire the vehicle information of the front road in advance by using the vehicle-road cooperative sensing system, can more accurately, real-timely and stably complete the control of the longitudinal distance for the scenes that the blind area of the vehicle crosses and enters the lane, and the like, and simultaneously, performs multi-sensor fusion by using the road-end sensor and the vehicle-end sensor to acquire more accurate front vehicle information; in addition, by using a sliding mode control algorithm based on an exponential approaching law, the control precision of the vehicle distance is improved, and the system buffeting and external disturbance in the control process of the longitudinal distance of the automatic driving are effectively solved, and the interference caused by nonlinear factors is effectively avoided, so that the performance of the control system is obviously improved, and the stability and the accuracy of the control of the longitudinal distance are improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a method for controlling a longitudinal distance between vehicles according to the present invention;
fig. 2 is a schematic flowchart of step S101 in the method for controlling a longitudinal distance between vehicles according to the present invention;
fig. 3 is a detailed flowchart of step S102 in the method for controlling the longitudinal distance between vehicles according to the present invention;
fig. 4 is a detailed flowchart of step S103 in the method for controlling the longitudinal distance between vehicles according to the present invention;
fig. 5 is a schematic diagram of sliding mode control of the longitudinal distance of the intelligent vehicle in the invention.
Detailed Description
In order that the manner in which the present invention is attained and can be more readily understood, a more particular description of the invention briefly summarized above may be had by reference to the embodiments thereof which are illustrated in the appended drawings.
Fig. 1 is a schematic flowchart of a method for controlling a longitudinal distance between vehicles according to an embodiment of the present disclosure. The method comprises a step S101 to a step S104, and the specific steps are as follows:
step S101: and performing multi-sensor information fusion on the targets acquired by the road end sensor and the vehicle end sensor, and screening out a plurality of targets after information fusion.
Specifically, sensor information fusion is a process of fusing data from multiple sensors. In this embodiment, the sensors include a road end sensor and a vehicle end sensor. The road end sensor comprises a road end camera, a road end radar and the like. The vehicle-end sensor comprises a vehicle-end camera, a vehicle-end radar and the like. The data of sensors such as a road end camera, a road end radar, a vehicle end camera, a vehicle end radar and the like are collected and fused to form a single model or image of the surrounding environment of the vehicle, so that the detection probability and reliability of objects around the vehicle can be improved, and the environment can be represented more accurately and more reliably.
Referring to fig. 2, the multi-sensor information fusion of the targets acquired by the road-end sensor and the vehicle-end sensor specifically includes the following steps:
step S101a: and (4) carrying out information inspection on the targets collected by the road end sensor and the vehicle end sensor, and filtering and removing the targets which do not meet the requirements.
Specifically, target information collected by a road end camera, a road end radar, a vehicle end camera and a vehicle end radar is checked, wherein the target information comprises timestamp tolerance check, position jump tolerance check, speed jump tolerance check and the like, and targets which do not meet the tolerance requirements are filtered and removed to complete filtering and removing of the targets.
Step S101b: sequencing is carried out according to the timestamps of the road end sensor and the vehicle end sensor, and the acquired targets circularly enter the linear Kalman filter to be subjected to target fusion.
Specifically, target information acquired by a plurality of sensors is sequentially placed into a linear Kalman filter according to the time stamp sequence of the sensors. Performing target prediction of the linear Kalman filter on a target in the linear Kalman filter, namely predicting the position, the speed, the acceleration and the like of the target after time t; then, target information acquired by the next sensor is put into a linear Kalman filter, and at the moment, mahalanobis distance calculation is carried out on the target in the linear Kalman filter and all sensing targets circularly entering the linear Kalman filter at present; then, for all perception targets of the current cycle, after Mahalanobis distance calculation, screening out an optimal target, and updating the target of the linear Kalman filter; and then, target fusion of the next sensor is carried out until the circulation is finished, so that the sensing target fusion of the road end sensor and the vehicle end sensor is finished. The targets after perception fusion are completed comprise fusion target ID, fusion target position, fusion target speed, fusion target acceleration, fusion target confidence, fusion target direction angle, fusion state of linear Kalman filtering and fusion target type.
After the information fusion of the sensors is completed, in order to reduce subsequent computational power, a plurality of targets subjected to information fusion can be screened out and used as target ranges for subsequently determining front car following targets. The specific screening method comprises the following steps: and carrying out priority ordering on the fused targets after information fusion according to the confidence, the Mahalanobis distance, the position standard deviation, the speed standard deviation, the acceleration standard deviation and the fusion state of the linear Kalman filtering, and screening out a plurality of targets from high to low according to the priority. In the present embodiment, up to 32 targets are screened out by priority.
Step S102: and determining a front vehicle following target of the self vehicle from the screened targets after the information is fused. Referring to fig. 3, the method specifically includes the following steps:
step S102a: and filtering and removing the targets which do not meet the requirement of the following targets in front of the self vehicle.
Specifically, the objects are filtered and removed according to the types of the objects (bicycles, pedestrians, automobiles, and the like), the linear kalman filter fusion attributes (predicted, co-estimated, updated, new, merge) of the objects, the directions (same direction, opposite direction, crossing, and the like) of the objects, the object ID jump of the front vehicle, the confidence degree of the objects, and the like.
Step S102b: and predicting the track of the vehicle according to the vehicle kinematics module, and calculating the transverse position deviation of the screened target and the vehicle.
Step S102c: and substituting the transverse position deviation into the lane contour model to determine a target which is closest to the self-vehicle in the left lane, a target which is closest to the self-vehicle in the right lane and a target which is closest to the self-vehicle in the lane where the self-vehicle is located (including a target crossing to the lane).
Step S102d: and predicting the time from the lane change to the lane in which the vehicle is located based on the transverse position and the transverse speed of the target closest to the vehicle in the left lane and the target closest to the vehicle in the right lane.
Step S102e: and comparing the predicted time with a preset threshold, if the predicted time is greater than the threshold, taking a target closest to the vehicle in a lane where the vehicle is located as a front vehicle following target of the vehicle, and if the predicted time is less than the threshold, taking a corresponding target as the front vehicle following target of the vehicle.
Specifically, taking an object closest to the own vehicle in the left lane as an example, if the time from the lane change of the object to the lane in which the own vehicle is located is less than a threshold value, the object is considered as a blind area and traverses into the own lane object. If the blind area does not exist, selecting a target closest to the vehicle in the lane where the vehicle is located as a front vehicle following target of the vehicle; if the blind area exists, the target of the front following vehicle is switched from the target which is closest to the vehicle in the lane where the vehicle is located to the blind area, and the target of the front following vehicle enters the target of the vehicle in the lane in a mode of crossing transversely. Thus, a fast response of the sliding mode control in the present application is achieved.
Step S103: the vehicle sliding mode control vehicle distance algorithm based on the exponential approximation law establishes a vehicle dynamics control equation, establishes a sliding mode surface, and calculates to obtain the ideal following acceleration of the vehicle based on the exponential approximation law.
Specifically, the sliding mode control is one of the nonlinear control methods, and has its own advantages, such as purposefully changing the system structure along with the change of deviation in the control process, ensuring that the system can finally reach and keep the system track on a preset sliding surface within a limited time under the action of a controller, so that the closed-loop system stably runs on the sliding surface, and thus the characteristic properties of the sliding mode are presented. Fig. 5 is a schematic diagram of sliding mode control of the longitudinal distance between the smart vehicles in the present application.
Characteristics of sliding mode control: a. the sliding mode design has no relation with the object parameters and external disturbance; b. the method is insensitive to the change and disturbance of object parameters, and does not need system identification and physical realization; c. the control response is fast.
Referring to fig. 4, the specific steps are illustrated as follows:
step S103a: and establishing a dynamic model for describing the longitudinal motion characteristics of the self-vehicle.
Specifically, an MATLAB simulation method is adopted to establish a dynamic model for describing the longitudinal motion characteristics of the self-vehicle. The kinetic model established in this example is as follows:
wherein, in the process,
represents the actual distance between the self-vehicle and the front vehicle-following target,
represents the ideal distance between the self vehicle and the front following vehicle target,
the safe time interval between the self vehicle and the front following target is shown,
indicates the vehicle speed of the preceding following vehicle target,
representing the difference between the actual spacing and the ideal spacing,
the relative speed of the vehicle and the front following target is shown, and u represents the ideal following acceleration of the vehicle.
Step S103b: and establishing a sliding mode surface equation, so that points outside the sliding mode surface can move to the sliding mode surface under a certain condition.
Specifically, the sliding-mode surface equation established in this embodiment is as follows:
wherein, in the step (A),
、
、
the parameters are set according to actual conditions.
Step S103c: and setting an index approach law to enable any initial point to approach the slip form surface.
Specifically, the exponential approximation law is used as the approximation law, so that the approximation speed is gradually reduced from a larger value to zero in the exponential approximation, the approximation time is shortened, and the speed of a moving point reaching a switching surface is small. The simple index approach, the approach of the motion point to the switching surface is a gradual process, the arrival within a limited time cannot be guaranteed, and the sliding mode does not exist on the switching surface, so that a constant-speed approach term needs to be added
When s is close to zeroWhen the approach velocity is
Instead of zero, a finite time of arrival may be guaranteed. The exponential approximation law set in this embodiment is:
and k is an exponential approaching term coefficient and is set according to the actual sliding mode surface approaching condition.
Step S103d: and preliminarily solving a control law equation according to the dynamic model, the sliding mode surface equation and the exponential approximation law.
Specifically, the control law equation obtained by the preliminary solution is as follows:
。
step S103e: smoothing the control law equation and adding a feedforward term.
Specifically, firstly, smoothing is performed on the control law equation to obtain:
. Then, the dynamic motion of the front following target is considered, the acceleration of the front following target is referred to as a feedforward term, because the sliding mode control does not consider that the front following target is a dynamically controlled system, and meanwhile, in order to adjust the influence of the feedforward term on the control system, the feedforward term needs to be calibrated, so that a coefficient is given
Obtaining:
wherein, in the process,
the acceleration of the front following target.
Step S103f: and calculating to obtain the ideal following acceleration of the self vehicle according to the control law equation.
Step S104: and controlling the self-vehicle to run at the ideal following acceleration so as to control the longitudinal distance between the self-vehicle and the front following target.
Specifically, the ideal following acceleration calculated in step S103 is input to an Electronic Stability Program (ESP) of the vehicle body of the vehicle, and after the ideal following acceleration is converted into a torque output, the ESP sends a torque request to a power system and a brake system of the vehicle, so that the vehicle runs according to the ideal following acceleration to control the longitudinal distance between the vehicle and the front following target.
Therefore, according to the technical scheme, the vehicle information of the front road can be acquired in advance by using the vehicle-road cooperative sensing system, the control of the longitudinal distance can be completed more accurately, in real time and stably for the scenes that the vehicles cross from a blind area and enter the lane and the like, and meanwhile, the road-end sensor and the vehicle-end sensor are used for carrying out multi-sensor fusion to acquire more accurate front vehicle information; in addition, by using a sliding mode control algorithm based on an exponential approximation law, the control precision of the vehicle distance is improved, and the system buffeting in the control process of the longitudinal distance of the automatic driving, external disturbance and interference caused by nonlinear factors are effectively solved, so that the performance of the control system is obviously improved, and the stability and the accuracy of the control of the longitudinal distance are improved.
In addition to the above embodiments, the present invention may have other embodiments; all technical solutions formed by adopting equivalent substitutions or equivalent transformations fall within the protection scope of the claims of the present invention.
Claims (3)
1. A vehicle longitudinal distance control method characterized by: the method comprises the following steps:
performing information inspection on targets acquired by a road end sensor and a vehicle end sensor, and filtering and removing the targets which do not meet the requirements;
sequencing according to timestamps of a road end sensor and a vehicle end sensor, circularly entering the acquired targets into a linear Kalman filter for target fusion, sequencing the priority of the fused targets after information fusion according to confidence, mahalanobis distance, position standard deviation, speed standard deviation, acceleration standard deviation and the fusion state of linear Kalman filtering, and screening out a plurality of targets from high to low according to the priority;
filtering and removing targets which do not meet the requirement of the following targets in front of the self vehicle;
predicting the track of the self-vehicle according to the self-vehicle kinematics module, and calculating the transverse position deviation of the screened target and the self-vehicle;
substituting the transverse position deviation into a lane contour model to determine a target closest to the vehicle in a left lane, a target closest to the vehicle in a right lane and a target closest to the vehicle in the lane where the vehicle is located;
predicting the time from lane change to lane of the vehicle based on the transverse position and the transverse speed of the target in the left lane closest to the vehicle and the target in the right lane closest to the vehicle;
comparing the predicted time with a preset threshold, if the predicted time is greater than the threshold, taking a target which is closest to the vehicle in a lane where the vehicle is located as a front vehicle following target of the vehicle, and if the predicted time is less than the threshold, taking a corresponding target as the front vehicle following target of the vehicle;
establishing a dynamic model for describing the longitudinal motion characteristics of the self vehicle:
wherein, in the step (A),
representing the actual distance between the vehicle and the front following target,
represents the ideal distance between the self vehicle and the front following vehicle target,
the safe time distance between the vehicle and the front vehicle-following target is shown,
indicates the vehicle speed of the preceding following vehicle target,
representing the difference between the actual pitch and the ideal pitch,
the relative speed of the vehicle and a front following target is shown, and u represents the ideal following acceleration of the vehicle;
establishing a sliding mode surface equation, so that a point outside a sliding mode surface can move to the sliding mode surface through a certain condition, wherein the sliding mode surface equation is as follows:
wherein the content of the first and second substances,
、
、
is a set parameter;
setting an index approach law to enable any initial point to approach to the slip form surface;
preliminarily solving a control law equation according to a dynamic model, a sliding mode surface equation and an exponential approximation law, wherein the control law equation is as follows:
；
smoothing the control law equation, and adding a feedforward term;
calculating to obtain the ideal following acceleration of the self vehicle according to the control law equation;
and controlling the self-vehicle to run at the ideal following acceleration so as to control the longitudinal distance between the self-vehicle and the front following target.
2. The vehicle longitudinal pitch control method according to claim 1, characterized in that: setting an index approach law, enabling any initial point to approach to the sliding mode surface comprises the following steps:
setting an exponential approximation law:
wherein, in the process,
and k is an exponential approximation term coefficient.
3. The vehicle longitudinal pitch control method according to claim 1, characterized in that: smoothing the control law equation, and adding a feed-forward term comprises:
and smoothing the control law equation to obtain:
；
adding a feed forward term to yield:
wherein, in the step (A),
the acceleration of the front following target.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108909709A (en) * | 2018-07-12 | 2018-11-30 | 奇瑞汽车股份有限公司 | Automatic follow the bus method and device |
| CN113359466A (en) * | 2021-06-30 | 2021-09-07 | 南通大学 | Fleet cooperative control method based on self-adaptive sliding mode control |
| CN113655718A (en) * | 2021-08-25 | 2021-11-16 | 的卢技术有限公司 | Self-adaptive control method for distance between automatic driving vehicles based on sliding mode control |
| CN114475608A (en) * | 2022-04-13 | 2022-05-13 | 清华大学 | Method and device for changing lanes for automatic driving vehicle, vehicle and storage medium |
-
2022
- 2022-09-21 CN CN202211150141.2A patent/CN115237147B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108909709A (en) * | 2018-07-12 | 2018-11-30 | 奇瑞汽车股份有限公司 | Automatic follow the bus method and device |
| CN113359466A (en) * | 2021-06-30 | 2021-09-07 | 南通大学 | Fleet cooperative control method based on self-adaptive sliding mode control |
| CN113655718A (en) * | 2021-08-25 | 2021-11-16 | 的卢技术有限公司 | Self-adaptive control method for distance between automatic driving vehicles based on sliding mode control |
| CN114475608A (en) * | 2022-04-13 | 2022-05-13 | 清华大学 | Method and device for changing lanes for automatic driving vehicle, vehicle and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 车辆多目标自适应巡航控制算法;张亮修等;《西安交通大学学报》;20161110(第11期);全文 * |
| 非奇异快速的终端滑模控制方法及其跟车控制应用;李升波等;《控制理论与应用》;20100515(第05期);全文 * |
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